Showing papers in "International Journal of Modelling, Identification and Control in 2016"

MPPT control in wind energy conversion systems and the application of fractional control (PI α ) in pitch wind turbine

Abstract: Wind power generation has grown at an alarming rate in the past decade and will continue to do so as power electronic technology continues to advance. In this article, this study proposes to ensure an optimal mechanical power generated by a wind turbine in a chain for a wide range of winds. The amount of power output from a wind energy conversion system depends upon the accuracy with which the peak power points are tracked by the maximum power point tracking (MPPT) method searches an optimal operating point from the slope of the power rotational. This can be achieved by implementing a pitch control in order to tap maximum energy at wind speeds lower than rated wind speed, since the proposed method selects the operating mode according to the wind speed. If the wind speed exceeds its rated value, blades are controlled to limit the electrical power to its nominal value. Pitch angle control is the most common means for adjusting the aerodynamic torque of the wind turbine. A fractional pitch angle controller is developed in this paper. The fractional control strategy may reach the potential when the system contains strong non-linearity, especially when the wind turbulence is strong. The controller PI classical and PIα fractional in pitch angle control are compared with the wind gust disturbance. The simulation tests were conducted using MATLAB/Simulink.

T-S fuzzy neural network predictive control for burning zone temperature in rotary kiln with improved hierarchical genetic algorithm

Abstract: How to control burning zone temperature of the lime rotary kiln is an important problem. In order to improve the control performance of burning zone temperature in lime rotary kiln, a predictive control method based on improved hierarchical genetic algorithm and T-S fuzzy neural network was proposed. This control method utilised T-S fuzzy neural network to build a nonlinear predictive model of burning zone temperature in rotary kiln. The predictive error is corrected through predictive output burning temperature, output feedback error and error correction. A fitness function is established by deviation and control variable. An improved hierarchical genetic algorithm was used for rolling optimisation of the optimal control variable. Simulation results show that the proposed predictive method has good control effect.

Multiple rules with game theoretic analysis for flexible flow shop scheduling problem with component altering times

Abstract: This paper studies the flexible flow shop scheduling problem with component altering times (FFSP-CAT), which is a specific form of the flexible flow shop scheduling problem with sequence dependent setup times in the practical scenario. Dealing with FFSP-CAT includes the jobs' machine assignment determination and the globe optimisation. We develop six rules for jobs' machine assignment, and since these rules will easily conflict with one another if they are used with the same priorities, we construct a repeated cooperative model and provide a game theoretical analysis, then derive a Nash equilibrium machine assignment scheme (NEMAS) to effectively manage these rules for jobs' machines assignment at each stage. Furthermore, to achieve the global optimum, we design an advanced compact genetic algorithm (ACGA). By combining NEMAS and ACGA, an approach named advanced compact genetic algorithm with Nash equilibrium machine assignment scheme (ACGA-NEMAS) is used for minimising the makespan of FFSP-CAT. Through extensive comparison experiments with different scales of instances, we show that the algorithm with NEMAS acquires 56.85% improvement over the algorithm without NEMAS, and ACGA-NEMAS performs 80.28% better than genetic algorithm.

A hybrid fuzzy-sliding mode controller for a mobile robot

Abstract: This paper deals with the autonomous navigation problem. Its objective is the driving of a mobile robot to the goal position without colliding with obstacles. Therefore, a new hybrid approach based on the combination of fuzzy logic systems with sliding mode method is proposed. More specifically, fuzzy logic is a reactive decision making method and it is devoted to bring the robot towards the target, whereas sliding mode is used to ensure the obstacle avoidance behaviour. The robot should follow accurately a limit cycle trajectory using a sliding mode controller. This limit cycle trajectory allows the generation of a smooth trajectory in the vicinity of obstacles. Through simulation results, we can conclude that the robot is able to generate suitable trajectories in different environments, which demonstrates the efficiency and the reliability of the proposed approach.

Pessimistic multi-granulation rough set-based classification for heart valve disease diagnosis

Abstract: The primary contribution of this study relies on proposing a new method, which can detect heart diseases in respective heart valve data. In this work, supervised quick reduct feature selection algorithm is applied for selecting important features from heart valve data. The classification method is applied only for relevant features selected using supervised quick reduct from heart valve data. In this paper, a new classification approach based on pessimistic multi-granulation rough sets (PMGRS) is applied for heart valve disease diagnosis. In multi-granulation rough sets, set approximations are well-defined by multiple equivalence relations on the universe, leading to an effective model for classification. This is confirmed by experimental evaluation, which shows excellent classification performance and also demonstrates that the proposed approach is superior to other benchmark classification algorithms including naive Bayes, multi-layer perceptron (MLP), and J48 and decision table classifiers.

Numerical software algorithms for monitoring control processes and correcting health by synthesis of hybrid fuzzy rules of decision-making on the basis of changes in energetic characteristics of biologically active points

Abstract: In contemporary medical practice, there is a growing interest in traditional oriental methods of reflexodiagnostics and reflexotherapy based upon teaching about the meridian energy balance of the body. In the present paper, new mathematical models are offered, which reflect the interaction between internal organs and biologically active points (BAPs) of meridian structures and make it possible to specify and analyse a wide range of diseases. Numerical software methods and algorithms are described for selecting BAPs and correcting health condition by acupuncture methods. The results show the best quality of decision-making is provided if the synthesis of fuzzy decision rules is carried out according to exploratory analysis intended for studying various features of data. The paper contains information about the way the prediction problem can be solved and how it is possible to deal with the need for early diagnosis of stomach diseases using the suggested software method and algorithms. Checking on testing sets with 100 persons for each class formed by highly-qualified experts showed that the diagnostic effectiveness of the solution exceeds 0.85. Such a result permits us to recommend the decision rules we have developed for practical application.

State estimation for distributed cyber-physical power systems under data attacks

Abstract: For the physical network, such as power physical network, built cyber network to optimise the regulation and control to constitute cyber-physical system, in order to achieve optimal control of large scale distributed systems, and bring along with virus, hacking, denial of service from the threat of cyber network that may lead to malicious damage in physical system. In this paper, a distributed dynamic model for a class of power systems with data attack is built with the assumption of attack detectability. Dynamic state estimators are presented to detect the attacked data and estimate its original value. Finally, a simulation of 9-bus power system shows the effectiveness of our proposed method.

Synchronisation of the hyperchaotic complex Lorenz system in a finite time

Abstract: In this paper, we investigate the finite-time chaos synchronisation of hyperchaotic complex Lorenz systems. The sufficient conditions for achieving the finite-time synchronisation of two chaotic systems are derived based on the theory of finite-time stability of dynamical systems. In fact, we propose a simple adaptive control method for realising chaos synchronisation in a finite time. It is shown that the technique introduced here can be further applied to various finite-time synchronisations between dynamical systems. Simulation results show the effectiveness of the proposed method.

A computational flow model of oxygen transport in the retinal network

Abstract: The retina's high oxygen demands and the retinal vasculature's relatively sparse nature are assumed to contribute to the retina's specific vulnerability to vascular diseases. This study has been designed to model the oxygen transport in physiologically realistic retinal networks. A computational fluid dynamics study has been conducted to investigate the effect of topological changes on the oxygen partial pressure distribution in retinal blood vessels. The Navier Stokes equations for blood flow and the mass transport equation for oxygen have been coupled and solved simultaneously for the laminar flow mass transfer problem. The mean oxygen saturation of a healthy eye has been 93% in retinal arterioles and 58% in venules. The arteriovenous difference has been 35%. For a patient with a central retinal vein occlusion (CRVO), the mean oxygen saturation has been 33%. The findings from the analysis are generally consistent with a lot of previous experimental measurements and clinical data available in the literature, demonstrating the efficiency of our model for predicting the oxygen distribution in the retinal networks. This paves the way for a new research and applications for simulating inaccessible cases from experimental studies.

Direct adaptive fuzzy sliding mode controller without reaching phase for an uncertain three-tank-system

Abstract: In this paper, a direct adaptive fuzzy sliding mode is proposed to design a new robust controller without reaching phase and chattering problems for a multiple-input multiple-output (MIMO) three-tank-system with unknown dynamics and external disturbances. The approach is based on modifying the sliding domain equation through the use of the Mamdani fuzzy logic approaches. The adaptive fuzzy law Takagi-Sugeno (TS) model is used to directly approximate the vector control of the system. Moreover the auxiliary sliding mode control term is incorporated in the control law to attenuate the fuzzy approximation errors and the external disturbances. The stability and robustness of the proposed control scheme are provided. Simulation results are presented which demonstrate the efficiency and robustness of the proposed control scheme.

On Zhou's maximum principle for near-optimal control of mean-field forward-backward stochastic systems with jumps and its applications

Abstract: This paper is concerned with stochastic maximum principle for near-optimal control of nonlinear controlled mean-field forward-backward stochastic systems driven by Brownian motions and random Poisson martingale measure (FBSDEJs in short) where the coefficients depend on the state of the solution process as well as on its marginal law through its expected value. Necessary conditions of near-optimality are derived where the control domain is non-convex. Under some additional hypotheses, we prove that the near-maximum condition on the Hamiltonian function in integral form is a sufficient condition for "-optimality. Our result is derived by using the spike variation method, Ekeland's variational principle and some estimates of the state and adjoint processes, along with Clarke's generalised gradient for non-smooth data. This paper extends the results obtained by Zhou (1998) to a class of mean-field stochastic control problems involving mean-field FBSDEJs. As an application, mean-variance portfolio selection mixed with a recursive utility functional optimisation problem is discussed to illustrate our theoretical results.

Influence of aeroelastic flow induced oscillations on fatigue life of an airplane wing structure

Abstract: In the field of aeroelasticity, the phenomenon of dynamic vibrations and its prevention is a primary challenge that is imposed on an airplane designer. The fully coupled/partly coupled fluid-structure interaction (FSI) analysis is one of the widely used techniques implemented by design industries to assess the characteristics of limit cycle oscillations (LCO). It requires detailed computational modelling capabilities including a high speed wind tunnel testing environment. Hence, the partly coupled (or) moderate loosely coupled (MLC) FSI techniques are preferred to approximate the real phenomenon better than the quasi-steady models. The turbulence characteristics of a viscous flow field are computed efficiently by a reduced-order modelling approach that offers tractable solutions. This novel approach is used to determine the consequences of LCO on the fatigue life of an airplane wing structure. Further, the numerical fatigue simulations are used to demonstrate the effectiveness of MLC algorithm against experimental techniques.

Non-prehensile manipulation planning of a three-rigid-link object using two cooperative robot arms

Abstract: This work presents the dynamic modelling, the static analysis, and a proposed control algorithm for non-prehensile manipulation of a three rigid link object manipulated by two cooperative robot arms in a plane. The system is configured so that one arm is in contact with two links, while the other arm is in contact with the third link. The purpose of the static analysis is to obtain the interaction forces as well as their locations required to hold the object for a specified configuration. The dynamic model of the system is introduced, and the static analysis, considering both the frictionless and frictional contact cases, is deduced. The effect of changing the direction of the static frictional forces at the multi contact points on the static problem is introduced. A PD controller with feed forward acceleration is proposed to perform the manipulation task. Simulation results show the validity of the proposed control scheme.

Full-order multi-input/multi-output terminal sliding mode control for robotic manipulators

Abstract: This study investigates the utility of the full-order terminal sliding mode in the field of robotic manipulator control. Two novel full-order multi-input/multi-output terminal sliding mode control schemes are proposed for position tracking and velocity tracking of rigid robotic manipulators. The first scheme uses the robust control to deal with the system uncertainties. The second scheme utilises the neural network to estimate the uncertain dynamics. Both of the two schemes can drive the system states to reach the designed full-order terminal sliding mode and then the tracking errors can converge to zero in finite time. Compared with the existing terminal sliding mode control techniques, the two control schemes proposed in this paper exhibit full-order dynamics and overcome singularity and chattering problems without sacrificing the tracking precision. Lyapunov stability analysis and simulation results are presented to demonstrate the effectiveness of the proposed control schemes.

Implementation of model predictive control for three-phase inverter with output LC filter on eZdsp F28335 Kit using HIL simulation

Abstract: The control of UPS inverters has a special importance in applications where a high quality output voltage is needed. Several control schemes have been proposed for the control of three-phase inverter. This paper presents a simple and powerful control scheme using model predictive control (MPC). It uses a discrete-time model of the system to predict the behaviour of the output voltage for all possible switching states generated by the inverter. Then, a cost function is used for selecting the optimal switching state that will be applied at the next sampling instant. The simulation results under linear and nonlinear loads are presented, using MATLAB/Simulink tools, verifying the feasibility and good performance of the proposed control scheme. Finally, experimental results are presented, using HIL simulation, to verify the feasibility and good performance of the proposed MPC under realistic conditions.

New solutions for optimal power production, distribution and consumption in smart grids

Abstract: This paper deals first with smart grids for electrical power networks, second with flexible and adaptive smart meters, and third with new green smart homes which offer original services. We propose a multi-agent architecture to manage the energy production, the peak consumption, and the problem of concurrent faults. Second, we propose an agent-based architecture for an STM32F4 device where a hierarchical software agent is defined to control the environment evolution before applying local reconfigurations for a required flexibility of the smart meter SM. The reconfiguration is assumed in practice to be any addition, removal or update of new services to or from SM such as the energy consumption, the remote information reading and power shutdown, the stabilisation of the delivered power, the management of new power provider offers, the sale of energy and finally the peak consumption management. Third, a slave agent is proposed for each selected home device to control its local consumption, and a unique master agent is proposed to control the whole architecture. We optimise the use of green energy and the consumption costs by exploiting the offers from providers and also peak times. A visual simulator named X-SG is developed and applied to a case study proposed by Cynapsys.

Design and evaluation of hybrid temperature control for cyber-physical home systems

Abstract: The design of control systems is crucial for improving the comfort level of the home environment. Cyber-physical systems (CPSs) can offer numerous opportunities to design highly efficient control systems. In this paper, we focus on the design and evaluation of temperature control systems. By using the idea of CPS, a hybrid temperature control (HTC) system is proposed. Through an energy efficient temperature control (EETC) algorithm, the HTC system maintains the room temperature in the desired interval. In the tight integration of physical and cyber worlds, the sensing accuracy of the physical platform has significant impact on the performance of the HTC system. Through simulations and field experiments, the relationship between control performance and sensing accuracy is captured. A fitting function method is proposed to improve the sensing accuracy without increasing the monetary cost of the system implementation. By using this method, the performance of the HTC system can be increased significantly.

Depth advanced control of an autonomous underwater robot

Abstract: Autonomous underwater robot control is a very challenging task because of autonomous underwater robot system nonlinearity, time-variance, uncertain external disturbance and difficulty in hydrodynamic modelling. Based on detailed autonomous underwater robot control survey and description of autonomous underwater robot dynamics, in this article we have used a high order sliding modes controller (HOSMC) for depth control of the autonomous underwater robot, based on the third order sliding modes. High order methods allow overcoming the chattering effect by removing the discontinuity of the control vector. We show that these high order controllers hold the properties of classical SM control laws and remove chattering problem. Different simulations have been carried out to show the performance and effectiveness of the proposed method.

Hybrid genetic algorithm-swarm intelligence-based tuning of temperature controller for continuously stirred tank reactor

Abstract: This paper presents a hybrid model of swarm intelligence and genetic algorithm for tuning of PID controller parameters for a temperature control of continuously stirred tank reactor which is generally used to carry out chemical reactions in an industry on a large scale. A time domain study of different methods for tuning of PID controller parameters was performed and it was found that particle swarm optimisation (PSO) performance was better than genetic algorithm (GA). But in recent literature hybrid mechanisms are used for tuning of controllers and a hybrid of GA-PSO gave the better output results in the observations hence it is proposed here to control the temperature of continuous stirred tank reactor.

Modelling and simulation of a quadrotor in V-tail configuration

Abstract: Standard quad-rotors are the most common and versatile unmanned aerial vehicles (UAVs) thanks to their simple control and mechanics. However, the common coplanar rotor configurations are designed for maximising hovering and loitering performances, and not for fast and aggressive manoeuvrings. Since the expanding field of application of micro aerial vehicles (MAVs) requires ever-increasing speed and agility, the question whether there are better configurations for aggressive flight arises. In this work, we address this question by studying the energetics and dynamics of fixed tilted rotor configurations compared to standard quad-rotor. To do so we chose a specific configuration, called V-tail, which is as mechanically simple as the standard X-4 quad-rotor, but has back rotors tilted by a known fixed angle, and developed the dynamical model to test its properties both through software simulation and with actual experiments. Mathematical modelling and field experiments suggest that this configuration is able to achieve better performance in manoeuvring control, while losing some power in hovering owing to less vertical thrust. In addition, these increases in performance are obtained with the same attitude control as the standard quad-rotor, making this configuration very easy to set up.

Fuzzy sliding mode fault tolerant control for a class of perturbed nonlinear systems

Abstract: This paper proposes an adaptive FTC approach which combines a sliding mode controller (SMC) and a proportional integral (PI) controller for nonlinear MIMO systems. The main contribution of the proposed method is that the structure of the controller system is partially unknown and does not require the knowledge of the bounds of uncertainties and disturbances. This method uses adaptive fuzzy systems to approximate the unknown nonlinear functions. Then, in order to reduce the chattering phenomenon without degrading the tracking performances, the discontinuous term in the conventional sliding mode technique is replaced by an adaptive PI term. According to Lyapunov approach, the dynamic tracking performance of the closed-loop system is optimised despite the presence of faults. Finally, the proposed method is applied to an inverted pendulum as a nonlinear SISO system and to a robot manipulator as a nonlinear MIMO system. It achieves satisfactory simulation results in both cases.

Robust power system stabiliser design via interval arithmetic

Abstract: In this paper, an interval arithmetic (IA) approach is proposed to rigorously address load uncertainties associated with the design of PSS. The proposed approach characterises the robust stabilising three-term/parameter PSSs computed for a single-machine infinite-bus system. A robust PSS can properly function over a wide range of operating conditions. Image-set and interval plant models are developed to express all uncertainties in the model parameters imposed by continuous variation in generation and load patterns. For robust PSS design purpose, an interval characteristic polynomial of the closed loop system is generated. Interval Routh-Hurwitz (RH) array is considered to get a fast but conservative calculation of the stability region in the controller-parameter plane using IA computation. Degenerate interval (RH) array for an image-set polynomial of the plant is thereafter developed where the region of robust stability is exactly computed. Simulation results are provided to confirm the effectiveness of the proposed approach in computing robust PSSs.

Robust diagnosis for hybrid noise systems

Abstract: This paper investigates the synthesis of robust hybrid observer dedicated to identification mode and fault detection and isolation (FDI) for MIMO hybrid dynamical system (HDS) modelled via hybrid automata. The observer design is divided into two modules. The first is used to identify the current mode and the second is synthesised around a dedicated observer scheme (DOS) to detect faults. In order to highlight the efficiency of linear matrix inequality (LMI) approach in synthesis of robust hybrid observer, a comparison between this technique and pole placement method is proposed. The performance required by the LMI approach is manifested by ensuring a minimum convergence time.

Adaptive gravity compensation and region tracking control of an AUV without velocity measurement

Abstract: In this paper, a region tracking controller has been proposed for an autonomous underwater vehicle (AUV) to ensure tracking within a constrained region. During tracking only position measurements are considered to be available. A pseudo velocity signal is generated using a filter to compensate for the velocity signal. For the mapping of the unknown persistent effects in AUV dynamics, i.e., to compensate these unknown forces, an adaptive signal has also been introduced. A Lyapunov function has been utilised to prove the stability of the closed-loop system. The controller ensures uniformly ultimately bounded stability of the system states. Simulation results prove the effectiveness of the proposed controller in terms of energy saving during region tracking.

Sliding mode control of longitudinal motions for underground mining electric vehicles with parametric uncertainties

Abstract: Nowadays, zero-emission electric vehicles become more and more attractive personnel transport vehicles for mining industry. Owing to the special working environment and complex road conditions, a robust controller needs to be designed to achieve a stable and reliable vehicle system for underground mining electric vehicles (UMEVs). This paper first presents the UMEV model with system uncertainties. Then, a new controller based on the sliding mode control (SMC) is designed for UMEVs to track the longitudinal velocity while maintaining longitudinal slip ratio in a desired linear region in the presence of the bounded system uncertainties. The comparison of the simulation results for both the proposed SMC controller and a traditional SMC controller shows that the proposed SMC controller has a better performance for the long-distance up/down slopes with varying rolling resistance coefficients.

Structure optimisation of input layer for feed-forward NARX neural network

Abstract: This paper presents an optimisation method for reducing the number of input channels and the complexity of the feed-forward NARX neural network (NN) without compromising the accuracy of the NN model. By utilising the correlation analysis method, the most significant regressors are selected to form the input layer of the NN structure. Applications of vehicle handling and ride model identification are presented in this paper to demonstrate the optimisation technique, and the optimal input layer structure and the optimal number of neurons for the NN models are investigated. The results show that the developed NN model requires significantly fewer coefficients and less training time while maintaining high simulation accuracy compared with that of the unoptimised model.

Scaling-based least squares methods with implemented Kalman filter approach for nano-parameters identification

Abstract: A single-input and single-output (SISO) controlled autoregressive moving average system with scaled input-output data is considered here. Recursive least squares (RLSs) methods were used to estimate the nanosized parameters of a SISO linear model using input-output scaling factors. Thus, a general identification technique, through scaling data, was produced. Different variations of the RLS method were tested and compared. The first RLS method used a forgetting factor and the second method integrated a Kalman filter covariance. Using the described method, in order to estimate the resistance, time constant and inductance, the latter two lying within the nano range, the input signal must have both a high frequency and a high sampling rate, in relation to the time constant. The method developed here can be used to identify the nano parameters characterising the linear model, while allowing for a broader sampling rate and an input signal with lower frequency. Simulation results indicate that the proposed algorithm is both effective and robust at estimating the nano range parameters. The most powerful contribution contained here is the provision of a scaled identification bandwidth and sampling rate for the detecting signal in the identification process.

Robust control design for ball screw system focusing on the friction model

Abstract: This paper proposes a method to solve practical problems of positioning control using a ball screw system. The practical problems contain two difficulties. First difficulty is that friction reduces the positioning performance. In this paper, the friction is divided into three components, which are a linear term including viscous friction, the Stribeck effect and a nonlinear term including static friction and Coulomb friction. The linear term friction can be considered in the framework of linear control synthesis. The nonlinear friction and the Stribeck effect are considered as disturbances. These are compensated by adding one integrator inside the controlled loop and loop shaping. The second difficulty is that the change in the mass of the load affects the system response. Moreover, it is shown that the viscous friction coefficient varies by some experiments. The controller is designed to guarantee the robust stability for uncertain parameters, which are the mass of the load and the viscous friction coefficient. The effectiveness of the proposed method is verified by simulations and experiments.

3D surface reconstruction of retinal vascular structures

Abstract: We propose in this paper, a three-dimensional surface reconstruction of a retinal vascular network from a pair of 2D retinal images. Our approach attempts to address the above challenges by incorporating an epipolar geometry estimation and adaptive surface modelling in a 3D reconstruction, using three steps: segmentation, 3D skeleton reconstruction and 3D surface modelling of vascular structures. The intrinsic calibration matrices are found via the solution of simplified Kruppa equations. A simple essential matrix based on a self-calibration method has been used for the fundus camera-eye system. The used method has eventually produced vessel surfaces that could be fit for various applications, such as applications for computational fluid dynamics simulations and applications for real-time virtual interventional.

Comparative study on three phase cascaded inverter for various bipolar PWM techniques

Abstract: This paper represents the comparison of carrier-based bipolar pulse width modulation technique such as phase disposition, phase opposition disposition, alternating phase opposition disposition, carrier overlapping and variable frequency for the choice of three phase nine level cascade multilevel inverters. In this paper, cascade multilevel inverter topology with a multiconversion cell consists of two unequal voltage sources and it produces nine level output voltages at load terminal. Simulation results are performed by using MATLAB/Simulink. It indicates that alternating phase opposition disposition pulse width modulation provides higher output voltage with less harmonic distortion for all pulse width modulation techniques. It is also observed that the carrier overlapping pulse width modulation technique provides higher fundamental RMS output voltage for all pulse width modulation techniques.